Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model for MetaTrader 5
A multi-task learning framework based on ResNeXt optimizes the analysis of financial data, taking into account its high dimensionality, nonlinearity, and time dependencies. The use of group convolution and specialized heads allows the model to effectively extract key features from the input data
Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Final Part) for MetaTrader 5
We continue to build the Hidformer hierarchical dual-tower transformer model designed for analyzing and forecasting complex multivariate time series. In this article, we will bring the work we started earlier to its logical conclusion — we will test the model on real historical data
Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Hidformer) for MetaTrader 5
We invite you to get acquainted with the Hierarchical Double-Tower Transformer (Hidformer) framework, which was developed for time series forecasting and data analysis. The framework authors proposed several improvements to the Transformer architecture, which resulted in increased forecast accuracy
Neural Networks in Trading: Memory Augmented Context-Aware Learning for Cryptocurrency Markets (Final Part) for MetaTrader 5
The MacroHFT framework for high-frequency cryptocurrency trading uses context-aware reinforcement learning and memory to adapt to dynamic market conditions. At the end of this article, we will test the implemented approaches on real historical data to assess their effectiveness
Neural Networks in Trading: Memory Augmented Context-Aware Learning (MacroHFT) for Cryptocurrency Markets for MetaTrader 5
I invite you to explore the MacroHFT framework, which applies context-aware reinforcement learning and memory to improve high-frequency cryptocurrency trading decisions using macroeconomic data and adaptive agents
Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (Final Part) for MetaTrader 5
We continue to implement the approaches proposed by the authors of the FinCon framework. FinCon is a multi-agent system based on Large Language Models (LLMs). Today, we will implement the necessary modules and conduct comprehensive testing of the model on real historical data
Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (FinCon) for MetaTrader 5
We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks
Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part) for MetaTrader 5
We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns
Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (FinAgent) for MetaTrader 5
We invite you to explore FinAgent, a multimodal financial trading agent framework designed to analyze various types of data reflecting market dynamics and historical trading patterns
Neural Networks in Trading: An Agent with Layered Memory (Final Part) for MetaTrader 5
We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of